Graph theoretic visualization of patient and health worker messaging in the EHR
Our analysis demonstrates the feasibility of applying graph theory to understand communication dynamics between patients and health workers and highlights the burden of EHR-based messaging.
Authors
- Bradley Gallaher
- Evan D. Schoenberg (ORCID: https://orcid.org/0000-0002-9573-1273)
- Yuanda Zhu (ORCID: https://orcid.org/0000-0001-7812-9216)
- May D. Wang (ORCID: https://orcid.org/0000-0003-3961-3608)
- Andrew Hornback (ORCID: https://orcid.org/0009-0009-5461-5848)
- David A. Gutman (ORCID: https://orcid.org/0000-0002-1386-8701)
- Arash Harzand (ORCID: https://orcid.org/0000-0002-9192-9338)
- Muhammad Zia ul Haq (ORCID: https://orcid.org/0000-0003-0338-9552)
- Blake Anderson
Institutions
- Georgia Institute of Technology (US)
- Emory University (US)
- World Health Organization Regional Office for the Eastern Mediterranean (EG)
- Switchboard of Miami (US)
Publication Details
- Journal
- Frontiers in Artificial Intelligence
- Published
- 2024-12-03
- DOI
- https://doi.org/10.3389/frai.2024.1422208
- Citations
- 3
- Primary Topic
- Electronic Health Records Systems
- Type
- article
- Field-Weighted Citation Impact
- 2.12